Convolutional neural networks (CNNs) have become popular especially in computer vision in the last few years because they achieved outstanding performance on different tasks, such as image classifications. We propose a nine-layer CNN for leaf identification using the famous Flavia and Foliage datasets. Usually the supervised learning of deep CNNs requires huge datasets for training. However, the used datasets contain only a few examples per plant species. Therefore, we apply data augmentation and transfer learning to prevent our network from overfitting. The trained CNNs achieve recognition rates above 99% on the Flavia and Foliage datasets, and slightly outperform current methods for leaf classification.
@article{arxiv.1712.00967,
title = {Leaf Identification Using a Deep Convolutional Neural Network},
author = {Christoph Wick and Frank Puppe},
journal= {arXiv preprint arXiv:1712.00967},
year = {2017}
}